The contour tree is a topological structure associated with a scalar function that tracks the connectivity of the evolving level sets of the function. It supports intuitive and interactive visual exploration and analysis of the scalar function. This paper describes a fast, parallel, and memory efficient algorithm for constructing the contour tree of a scalar function on shared memory systems. Comparisons with existing implementations show significant improvement in both the running time and the memory expended. The proposed algorithm is particularly suited for large datasets that do not fit in memory. For example, the contour tree for a scalar function defined on a 8.6 billion vertex domain (2048 × 2048 × 2048 volume data) can be efficiently constructed using less than 10GB of memory.

Gaze-based selection has received signifcant academic attention over a number of years. While advances have been made, it is possible that further progress could be made if there were a deeper understanding of the adaptive nature of the mechanisms that guide eye movement and vision. Control of eye movement typically results in a sequence of movements (saccades) and fxations followed by a 'dwell' at a target and a selection. To shed light on how these sequences are planned, this paper presents a computational model of the control of eye movements in gaze-based selection. We formulate the model as an optimal sequential planning problem bounded by the limits of the human visual and motor systems and use reinforcement learning to approximate optimal solutions. The model accurately replicates earlier results on the efects of target size and distance and captures a number of other aspects of performance. The model can be used to predict number of fxations and duration required to make a gaze-based selection. The future development of the model is discussed. CCS CONCEPTS• Human-centered computing → HCI theory, concepts and models; User models.

In this chapter we explore the potential advantages of modeling the interaction between a human and a computer as a consequence of a Partially Observable Markov Decision Process (POMDP) that models human cognition. POMDPs can be used to model human perceptual mechanisms, such as human vision, as partial (uncertain) observers of a hidden state are possible. In general, POMDPs permit a rigorous definition of interaction as the outcome of a reward maximizing stochastic sequential decision processes. They have been shown to explain interaction between a human and an environment in a range of scenarios, including visual search, interactive search and sense-making. The chapter uses these scenarios to illustrate the explanatory power of POMDPs in HCI. It also shows that POMDPs embrace the embodied, ecological and adaptive nature of human interaction.

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